With rapid advances in wireless networking technologies, it is becoming increasingly feasible to deploy wireless multi-hop mesh networks, including distribution networks of utilities such as electricity, water, and gas, for real-time monitoring, reliability, and better resource management.
The emergence of smart grids offers a formidable challenge in deploying a distributed network of renewable power generation sources and then managing a distribution network for smooth, reliable, and optimized energy management over large geographic areas. A crucial component needed for the successful operation of the overall grid is to collect real-time data on resource consumption in different geographic areas.
A smart meter network typically includes a set of data sources in the form smart meters, and one or more data sinks, connected wirelessly, or through a wired network, such as power line communications (PLC) system. The smart meter acts as communication routers to relay source data, as well as data received from adjacent neighboring nodes.
Smart meters and data sinks are fixed in terms of geographical locations. Unlike conventional wireless sensor network and mobile ad hoc network, there are only two types of data flows in the smart meter network. The smart meters periodically send data to one or more designated data sinks. Data sinks can send control data to one or more smart meters as needed. The data sinks can be multiple hops away from some smart meters. Therefore, the data from these smart meters must travel over multiple hops before actually reaching the data sink.
The smart meter can possibly aggregate its own data and the data received from several of its neighbors before forwarding the aggregate data to the next hop neighbor, which can be a smart meter or a data sink. The data sinks can include processors and transceivers for processing received data, and sending the aggregate data to the operation centers of utility companies.
Routing is one of key functions in smart meter networks. Routes between smart meters and data sinks must be discovered before data communication can proceed. Routing in a real time smart meter network provides more challenges.
A number of routing methods are known for wireless sensor networks and mobile ad hoc networks based on different requirements, such as ad-hoc on-demand distance vector routing, dynamic source routing, temporally-ordered routing, and routing method for low power and lossy network.
However, conventional routing methods do not suit smart meter network well. Therefore, it is desirable to develop a routing method that satisfies the requirements of smart meter networks.
Routing in Smart Meter Network
In modern utility distribution networks, every smart meter is expected to periodically send its data to the operation center of the utility provider. A meter can also respond to abnormal events, such as a safety and security events by generating asynchronous data traffic. The collected data are then processed, analyzed, and utilized for better demand-response, resource management, and dealing with hazardous situations.
It is imperative that the data be communicated to data sinks in a reliable and efficient manner. Lost or excessively delayed data packets can cause waste of resources or, even worse, endanger human life and property. Therefore, the smart meter network must deploy a well-defined, efficient, and extremely reliable routing methods to satisfy the stringent performance requirements of smart meter network.
Problem with Routing in Smart Meter Network
A number of routing methods for wireless sensor networks and mobile ad-hoc networks are known. The smart meter networks, however, have very distinct requirements based on pronounced architectural differences from general wireless sensor networks, and mobile ad-hoc networks.
In contrast with peer-to-peer communications in wireless sensor networks, the smart meter network has only few data sinks, and almost all the traffic is directed towards the data sinks points. Also, the number of nodes in a smart meter network can run to thousands. Interference could be severe in such a large and dense network. Mobile objects, such as vehicles, can also cause communication link to fail. Data sending interval can be as short as very few minutes. Control data latency can be in seconds.
In peer to peer communications, each pair of communicating nodes generally discovers a route between the nodes. Route discovery in many such networks is done through a network-wide flooding. In a large scale network, communication overhead for route discovery would be huge, especially when many nodes communicate in peer-to-peer fashion. Such an excessive overhead must be avoided in large smart meter networks. Also, meter data and control data must be reliably delivered to destination. Therefore, nodes must have multiple alternate routes already discovered for use when the primary route to a sink point fails.
Furthermore, a node may need to discover routes to multiple data sinks. All these multiple routes must be discovered with minimal communication overhead during the initial discovery or setup phase. A failed route must be repaired without causing network-wide flooding of packets and without disrupting the regular periodic data communications.
One of commonly used routing methods for wireless ad hoc networks is ad-hoc on-demand distance vector (AODV) routing. AODV has its advantages, such as discovering a route only when it is needed and repairing a failed route automatically. However, AODV has excessive transmission of control messages. In AODV routing algorithm, each route discovery between a source node and a destination node causes one network-wide flooding. Route rediscovery takes considerably long time. It is a single path routing method. AODV suits peer-to-peer networks.
Dynamic source routing (DSR) is another routing method commonly used for wireless ad hoc networks. It is similar to AODV in that it forms a route on-demand. However, it does not rely on a routing table. Rather, the sender/source node includes the path to the destination in the packet being transmitted. DSR performs well in small and static networks with low mobility. However, its performance degrades rapidly with mobility increasing. Route maintenance does not repair a failed route. Connection setup delay is higher. Considerable routing overhead is involved due to the route information included in the data packet.
There are two well known routing methods based on directed acyclic graph (DAG). One is the temporally-ordered routing algorithm (TORA), which attempts to achieve a high degree of scalability using a non-hierarchical routing method. TORA constructs and maintains a DAG rooted at a destination. It achieves loop-free multipath routing by only allowing message flowing from nodes with higher heights to nodes with lower heights. TORA is good for dense networks. However, control message overhead in TORA is even higher. As number of nodes increases, control message overhead increases considerably. Also, TORA does not use a shortest path solution.
Routing for low power and lossy network (RPL) is another routing method using the DAG. RPL is under development by the Internet Engineering Task Force (IETF). RPL aims to provide providing reliable and low latency support for large scale smart grid networks. In RPL, each node maintains its position in a DAG structure by using a rank property. However, many important issues are left unresolved by RPL. Rank computation is not described for the RPL. Selection of parents is only based on receipt of a control message from a gateway. If no stable link exists, then this can cause delivery failures. There is no broadcast mechanism in RPL. RPL is tightly related to IPv6, which can cause RPL not suitable for non-IP networks.
Better approach to mobile ad hoc network (BATMAN) is also a routing method for a wireless ad hoc network. The crucial point of BATMAN is the decentralization of knowledge about the best route through the network. No single node has all the data. On a regular basis, each node broadcasts originator message to inform its neighbors about its existence. Neighbors then relay this originator message to their neighbors. In order to find the best way to a certain node, BATMAN counters the originator messages received and records which neighbor the message came in through. BATMAN exhibits high level of stability. However, BATMAN has slow convergence time. Periodic originator messages increases control message overhead, especially for large scale networks such as smart meter networks. Ant agents for hybrid multipath routing (AntHocNet) is an adaptive routing method for wireless ad hoc networks. It is a hybrid method by combining reactive route setup with proactive route probing, maintenance and improvement. AntHocNet discovers multiple paths and continuously searches for new paths. But, route repair takes multiple broadcasts that floods network. Periodic neighbor control messages adds control message overhead, which results in reliability and latency issues in large scale networks.